Institution: Graduate School at Faculty of Business, Economics and Social Sciences – Universität Hamburg
Lecturer: Prof. Dr. Kai-Uwe Schnapp (WiSo-Fakultät, UHH)
Schedule: Mo., 13.02.2017, 10:30 – 16:00 Uhr
Di., 14.02.2017, 09:00 – 15:00 Uhr
Mi., 15.02.2017, 09:00 – 15:00 Uhr
Do., 16.02.2017, 09:00 – 15:00 Uhr
Place: Universität Hamburg, further information in Geventis
Registration: You can register for the course until 15.12.2016 (13 Uhr) via Geventis
In their education students do regularly get to know statistics to a certain extent. Ideally, some data analysis will be done with data sets ready made for teaching. The resulting knowledge of statistics differs from university to university, but usually it is sufficient for at least simple tasks in data analysis. Actually starting with an analysis of one’s own, however, all too often is a bit rocky. The reason for this being that just knowing statistics is not enough. Why is that so? Self-collected data usually do not come as ready-made as it appears in statistics classes. They need to be adapted, transformed, aggregated or disaggregated, thoroughly documented and saved in meaning- and useful partitions. This involves a large number of tiny steps and decisions in the work process. It is all too easy to lose track of what has been done when, how, why and with which result. Why has X been filtered, why has Y been aggregated the way it has been aggregated, and where does the correction in Z come from and how has it been justified? After a few days it is often not clear anymore, why a variable does now look the way it does. And be it for the reason that the seed number for some random number generator has either not even been set or at least not been saved. It gets much more inconvenient later on, when an article is ready for publication and the journal is asking for documentation or even a replication data set. Or, when an interested reader is sending an e-mail, asking politely for more detailed information on data preparation and analysis. Because it is now that the search starts for information that has been lost along the way.
Many of those problems can be avoided by a well-conceived and thoroughly developed plan for data manipulation and analysis accompanied by extensive documentation of every step in the work process. Most if not all of the things one has been doing can be kept within reach when the steps in the work process are clear (and standardized to the extent possible), and when saving and documenting becomes part of the daily routine of working with data.
This course will try to introduce students to such a working-method with data and at the same time do the first steps of data manipulation and analysis with STATA. The aim is not, however, to actually teach statistics. It is assumed that the students already have at least a basic knowledge of statistics with at least some descriptive and inferential methods known to everybody. Basic knowledge of regression analysis is mandatory.
The structure of STATA’s command language, work process and documentation will be presented. The course teaches some tricks how to achieve almost directly a publishable output. A special focus will be put on graphical output and its improvement.
You may bring your own data into the workshop. However, having data of one’s own is not required.
You will get to know: – STATA’s user interface – basic knowledge in data handling and data manipulation with STATA – basic knowledge of the structure and workings of STATA’s commands for data analysis – basic knowledge of how to quickly produce publishable output with STATA – basic knowledge of efficient process management and documentation using STATA
After all, the course is a language course of sorts. You will get to know STATA as a language to code your data analysis.
The course will be held in a computer lab. All steps will be demonstrated by the instructor and directly applied by the students. There will be room for free but guided exercise.
There will be a brief (90 minutes) intro into the Graphical User Interface of STATA for people who have never worked with STATA before on the first day of the course week. The course itself starts on the second day of that week, so that newbies have an afternoon’s time to achieve a little acquaintance based on the intro in the morning.
As introductory literature and a good guide book for further work I suggest Kohler/Kreuter „Data analysis using STATA“ (meanwhile in its third edition) or K/K „Datenanalyse mit STATA“ (the fourth edition from 2012 is strongly recommended).
In order to earn credits students will have to complete a homework of about 5-15 hours (depending on individual work pace and prior knowledge) with some data manipulation and analysis, the production of some output that is (almost) ready for publication and a thorough documentation of the whole process.